I work on Artificial Intelligence helping machines infer and interpret context from as few inputs as possible. I firmly believe that strong AI is possible only with significant improvement in our current ability to model and infer context. Most of current research is focussed on Semantic Intelligence, building sophisticated knowledge graphs and inference algorithms to figure out precise and highly granular context from content(videos, images but mostly textual). I have extensively worked on almost every problem in NLP ranging from POS tagging, chunking to word sense disambiguation, entity recognition, sentiment analysis, document classification, review mining, text summarization. Most of this work has been inducted into Cogmeta, our audience intelligence platform to be launched soon. I also seriously dabble into alternative models of computation and hardware efficient for semantic and cognitive computing. I also work on large scale machine learning algorithms specifically real time regression, classification and clustering algorithms/techniques for massive data sets. I am primarily interested in techniques that employ high degree of parallelization and randomization to obtain approximate solution in real time. Specialties: Deep learning,Behavioral Targeting, Distributed Machine Learning Algorithms, Classifiers/Regression Algorithms for Web Scale Data, Advanced NLP Algorithms (Named Entity Recognition, Sentiment Analysis, Relationship Extraction), Hadoop, NoSQL Databases (Cassandra/HBase), Cloud Computing, Recommendation Engines, Distributed Computing, Statistical Data Visualization, Neuromorphic models of computation